Quantum Computing: Countdown to a Quantum Leap · too vast to solve. But it’s not a panacea, and huge hurdles lie in bringing it to mainstream adoption. However, it is increasingly

Quantum Computing: Countdown to a Quantum LeapDr. Owen Rogers, Research Vice President, Cloud Transformation

James Sanders, Analyst, Cloud Transformation

Rachel Dunning, Research Associate

Matthew Utter, Research Associate

Quantum computing would make a profound impact to society’s viewpoint

of what is possible, particularly in scientific fields where problems are simply

too vast to solve. But it’s not a panacea, and huge hurdles lie in bringing it

to mainstream adoption. However, it is increasingly in the public eye, with

big players looking to educate the next generation, and startups receiving

speculative funding. Now is the time to experiment and to keep an open

mind, but don’t expect miracles anytime soon.

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MAR 2020

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Quantum Computing: Countdown to a Quantum Leap© C O P Y R I G H T 2 0 2 0 4 5 1 R E S E A R C H . A L L R I G H T S R E S E R V E D.

About the AuthorsDr. Owen Rogers

Research Vice President, Cloud Transformation

As Research Vice President, Owen Rogers leads the firm’s Digital Economics Unit, which serves to help customers understand the economics behind digital and cloud technologies so they can make informed choices when costing and pricing their own products and services, as well as those from their vendors, suppliers and competitors. Owen is the architect of the Cloud Price Index, 451 Research’s benchmark indicator of the costs of public, private and managed clouds, and the Cloud Price Codex, our global survey of cloud pricing methods and mechanisms. Owen is also head of 451 Research’s Center of Excellence for Quantum Technologies. Owen has previously held product management positions at Cable & Wireless and Claranet, and has developed a number of hosting and cloud services. He is a Chartered Engineer, a Member of the British Computer Society and a member of the Royal Economic Society. In 2013, he completed his PhD thesis on the economics of cloud computing at the University of Bristol. Owen was named ‘Innovative Analyst of the Year’ in the Institute of Industry Analyst Relations’ global awards in 2018.

James Sanders

Analyst, Cloud Transformation

Rachel Dunning

Research Associate

Matthew Utter

Research Associate

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Key Findings• Quantum computing will have profound impact on technology and even society if (and we

emphasize if) it becomes mainstream. However, there are still huge hurdles to overcome. We think we are at least a decade away from quantum computing solving a problem that is currently unsolvable in useful timeframes using a traditional computer.

• A quantum computer allows all states to encoded in the uncertainty of the characteristics of a subatomic particle and then evaluated once, as opposed to a traditional computer where each state must be encoded and evaluated separately. Many problems that would take too long to solve today (measured in thousands of years) could be solved in a matter of minutes or hours. The challenge is in getting such small particles under control, and preventing tiny interactions corrupting the data we are trying to process.

• Investments from the likes of IBM, Microsoft, Google, Amazon and Honeywell are unlikely to have made a financial return yet, but if quantum computing becomes mainstream, the returns will be significant.

• Quantum computing vendors are informing and educating the next generation for when quantum computing is commercially viable. Vendors hope this gives them the ability to capitalize when the technology becomes mainstream.

• The fundamental use case of quantum computing is to solve problems with so many permutations that they can’t be processed in human timeframes. We believe the key use cases will be scientific (chemical engineering, physics, biochemistry, healthcare, materials engineering and mathematics), where there is a need to compute and resolve a vast quantity of states.

• Quantum computers should be thought of as an entrant in the class of compute accelerators that provide specific capability in a hybrid model alongside general-purpose processors. Increase in adoption and reduction of the costs of FPGAs, ASICs and GPUs may make quantum computing’s economic argument less clear cut.

• Although cracking keys is often cited as a use case, new quantum-proof algorithms, technologies, and even simply increasing key lengths in some cases can effectively mitigate this threat today and in the longer term.

• Our recommendation is that companies with even a slight interest should free up one of their developers to experiment for a few hours a week, just so they are better placed to identify quantum computing opportunities. Those that already have a problem in mind that promises a big return should test the waters with quantum computing vendors.

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Executive Summary Introduction

Quantum computing has been the subject of research since the early 1980s, and – theoretically – there is a huge basis to believe that the use of particles of unimaginable smallness could unlock problems that have been out of reach in realistic timeframes using traditional computing models. However, there are massive practical challenges in controlling these tiny particles for long enough to actually make them do what we want. Simply put, there are no guarantees that we can use them to solve practical problems.

Yet the business case is strong – a practical quantum computer would open the door to incredible opportunities in physics, chemistry, machine learning, finance, healthcare and beyond. While no panacea, viable quantum computing would increase our ability to attack previously insurmountable computational challenges, from the structure of materials to the folding of proteins to nature’s ability to produce chemical reactions, which would be impractically time-consuming or impossible even with the deterministic computers we all use today.

We are still years away from creating such value with quantum systems, if it’s at all possible. But it’s telling that those investigating quantum computing are speaking up more and more. IBM, Google, Microsoft, AWS, Honeywell and other established players are making big announcements, even squabbling over relative progress. Startups are being funded to investigate technologies and even software development. Quantum computing is a gamble and there are no guarantees, but for such a huge payoff, it is probably a risk worth taking.

This report takes a look at the current state of quantum computing for a general audience. Quantum computing is a highly mathematical discipline and, unfortunately, our grasp of common sense in the macroworld we live in breaks down in the microworld. Our advice is to suspend preconceptions of what is possible and just accept that at such tiny distances, experiences and potentialities are different. The ‘Theory of Everything,’ which aims to reconcile physics at the macroworld and microworld level, shows this is a problem that isn’t just baffling for normal people, but for physicists too. Our aim is to enable a technology- or business-focused reader to gain a general understanding of the concepts.

This report relies on analogies to simplify the complexities and minutiae of quantum computing. Our representation of quantum computing is practical and utilitarian, intended as a fundamental primer for technology leaders to understand the opportunities and market. By its very definition, the ‘state of the art‘ of quantum computing is constantly iterating and evolving, though for present state, most industries should wait attentively for the right moment to invest capital.

Quantum computing is a

gamble and there are no guarantees,

but for such a huge payoff, it is

probably a risk worth taking.

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Presently, we strongly encourage investing time. Experimentation in quantum computing – if even just a side project from a single developer interested in exploring frameworks and cloud-based quantum computers detailed in this report – can open the mind to future opportunities and potentialities when (if) quantum computing becomes mainstream. Allowing developer time to be spent now can provide an early – if small – advantage, at a tiny cost. Enterprises with a specific problem in mind have nothing to lose (and potentially much to gain longer term) by engaging vendors now.

The great physicist Richard Feynman said, “If you think you understand quantum mechanics, you don’t understand quantum mechanics.” Don’t try to get all the details today, just open your mind to the possibilities.

Methodology

Following the launch of our Quantum Computing Centre of Excellence in 2019, the 451 Research Associate team set about collecting data from websites on companies working on quantum computing, be it as vendors selling hardware, providers offering quantum services, enterprises running proofs of concept, or startups dabbling in development. From this extensive list, 451 Research focused on companies selling or developing groundbreaking quantum technology and organized briefings supplemented with further investigation from public sources. The aim of this report is not to rank what is a very emerging market, but to show the current state of the art and educate business leaders on its potential impact.

Reports such as this one represents a holistic perspective on key emerging markets in the enterprise IT space. These markets evolve quickly, though, so 451 Research offers additional services that provide critical marketplace updates. These updated reports and perspectives are presented on a daily basis via the company’s core intelligence service, 451 Research Market Insight. Forward-looking M&A analysis and perspectives on strategic acquisitions and the liquidity environment for technology companies are also updated regularly via Market Insight, which is backed by the industry-leading 451 Research M&A KnowledgeBase.

Emerging technologies and markets are covered in 451 Research channels including Applied Infrastructure & DevOps; Cloud & Managed Services Transformation; Customer Experience & Commerce; Data, AI & Analytics; Datacenter Services & Infrastructure; Information Security; Internet of Things; and Workforce Productivity & Collaboration.

Beyond that, 451 Research has a robust set of quantitative insights covered in products such as Voice of the Enterprise, Voice of the Connected User Landscape, Voice of the Service Provider, Cloud Price Index, Market Monitor, the M&A KnowledgeBase and the Datacenter KnowledgeBase.

All of these 451 Research services, which are accessible via the web, provide critical and timely analysis specifically focused on the business of enterprise IT innovation.

For more information about 451 Research, please go to: www.451research.com.

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Table of Contents1. Introduction to Quantum Computing 1

Schrödinger’s Cat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

The Quantum Treasure Chest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Figure 1: Brute-Force Problem-Solving Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Figure 2: Quantum Problem-Solving Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Getting Down to Qubits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Quantum Gates: The Standard Universal Computer Architecture . . . . . . . . . . . . . . . . . 5

Quantum Annealing: A Specific Architecture for Specific Problems. . . . . . . . . . . . . . . . 6

2. Success Factors 7

Benchmark Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 3: Most Overhyped Security Buzzwords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Role of Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Figure 4: A Minority of Providers Are Considering Quantum Computing . . . . . . . . . . . . . . 10

Measuring Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

3. Types of Qubit 13

Superconducting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Trapped Ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Spin Qubits/Quantum Dots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Topological . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

4. Hardware Is the Battleground 15

IBM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

D-Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Rigetti Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Honeywell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

IonQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Quantum Circuits, Inc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

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5. Development and Services Open the Door 21

Qiskit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Q# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Amazon Braket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Azure Quantum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

1QBit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

6. Big Tech Companies Research in the Background 24

Microsoft. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Amazon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Google . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Intel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

7. The Post-Quantum Era 26

Security Threat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

Quantum Key Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27

8. Recommendations 30

9. Further Reading 31

Appendix A – Companies Primarily Developing Quantum Computing Hardware and/or Systems 32

Appendix B – Companies/Projects Primarily Focusing on Quantum Computing Software Development 39

General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Vertical-Specific. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

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1. Introduction to Quantum ComputingSchrödinger’s Cat

Consider a thought experiment involving a cat – Schrödinger’s cat. The cat is placed in a box, along with a radioactive material. When a sensor detects the radioactive material has radiated a particle, a device releases toxic gas into the box, killing the poor cat. We put the cat into the box alive, close the lid and wait. Is the cat alive or dead?

There is no way of knowing unless we measure the cat’s state. We could open the box, acoustically listen for movements or x-ray the box. But these are all measurements, and we genuinely have no way of knowing if the cat is alive or dead unless we penetrate the box using such measurements. We can determine the probability of the cat being alive or dead after a certain time, but we can never be certain. Even if we knew everything about the state of the radioactive element before the cat goes in, we would not be able to know for sure when the fatal radioactive particle is released – in other words, the atomic and subatomic particles that determine when a radioactive particle is released have inherent unpredictability. In essence, until we measure, the cat is both alive and dead, in a state of what is called superposition.

At the subatomic level, this is actually the case – some particles don’t necessarily have an absolute state, which prompted Einstein’s famous quote, “God does not play dice.” He couldn’t accept that some things are just inherently unpredictable. Even if we have all the data there is, we cannot determine what will come next. In the vacuum of space, particles can appear out of nowhere and disappear within millionths of a second because of this inherent unpredictability. It is this superposition of all possibilities due to inherent uncertainty that gives quantum computing its potential.

The Quantum Treasure Chest

Imagine we have an impenetrable chest, in which lies a treasure of vast importance – perhaps a cure for cancer, or the key to nuclear fusion. To access the chest, we must find the correct three-number combination of the padlock. If we don’t know the combination, the only way to access the chest is to try every possible combination from 000 to 999. But our chest is miles underground, and not easily accessible. In fact, to test every single combination, it takes a whole year of planning and implementation. In the worst case, if the code were 999, we’d have to wait 1,000 years to access the secrets inside (see Figure 1).

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Figure 1: Brute-Force Problem-Solving ApproachSource: 451 Research, 2020

Building a business case for investing in opening the chest is not feasible. Who would make an investment now for a return in – potentially – a thousand years? The payback period simply isn’t worth the effort.

But imagine we have a magic box – Schrödinger’s box. The box is special because it can make virtual ‘copies’ of the chest padlock, which represents inherent uncertainty. When we put the padlock in the box, 1,000 copies of the padlock are made – not real copies that we can access, but virtual padlocks encoded in the uncertainty of tiny particles. Now imagine we can manipulate the box: fire lasers at it or expose it to magnetic fields such that we can simultaneously manipulate all these copies, without actually opening the box or examining the inside. In our scenario, we could perhaps manipulate the combinations of the padlocks, such that each virtual padlock has a different code. This is akin to a quantum algorithm or ‘circuit.’ If we correctly manipulate the box, when we open it, these virtual copies disappear and all we see is the single padlock with the correct combination (see Figure 2). If we keep opening and closing the box and sample the results, we can become increasingly confident that the combination is the correct result.

0 0 0

00 1

00 2

06 1

1 YEAR598 YEARS

1 YEAR

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Figure 2: Quantum Problem-Solving ApproachSource: 451 Research, 2020

The crucial point about the box is we don’t have to test every combination. The box takes advantage of the uncertainty of the position and movement of subatomic particles to test every combination for us. So rather than having to do 1,000 tests, each taking a year, we can do a single test that simultaneously looks at all combinations. The speed-up here is effectively from 1,000 years to 1 year – is it worth investing in opening the padlock to cure cancer? Yes, it is likely this investment would make a significant return.

This is the quantum computer. In a nutshell, a quantum computer allows us to exploit uncertainty in the characteristics of a subatomic particle by performing operations on this uncertainty. Once these operations are performed, the characteristics of the particle can be measured, and a result obtained. The key part to remember is that a quantum computer allows all states to encoded in this uncertainty and then evaluated once, as opposed to a traditional computer where each state must be encoded and evaluated separately. This provides a huge (in fact, exponential) improvement in the time required to solve a problem.

0 0 0

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99 9

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MANIPULATIONS

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06 1? ? ?

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When the numbers get bigger, so too does the benefit of quantum computing. If the combination lock is eight digits, it would take us 9,999,999 years to brute force every combination. In the quantum computer ‘box,’ it would take a year. But the problem is the box has to be able to handle so many combinations. This is the challenge facing quantum computing – making it powerful enough to solve bigger combinations such that it provides a return on the investment.

Getting Down to Qubits

In computing, binary digits (or bits) are used to represent data: 0 or 1. In quantum computing, qubits are used, and these can represent 0, 1 or a superposition of 0 and 1. Until a qubit is measured, it is both 0 and 1, just like our cat is both alive and dead. It is this superposition that gives quantum computing an advantage over traditional computing (or Turing machines, more technically).

If we have to store the state of all three-bit numbers in a register to be processed, then we need to store and process all successive states: [000,001,010, etc., to 111], which in decimal represents [0,1,2...7] on traditional computers – essentially, we have to store all of them in the same way we have to look through all the padlock combinations, and then process each one individually. The same situation for three qubits is simply [???], with ‘?’ representing a superposition of both 0 and 1 (i.e., hidden in the box with its state unknown). We put the unknown [???] in the box so that the box makes virtual copies of all the possible combinations from [000] to [111], and then manipulate the box to solve our problem. When we open the box, the [???] becomes a definite answer.

If we are trying to hack security keys, for example, rather than going through every possible bit combination using a Turing machine, we can simply determine it once from the superposed qubits. We just need to manipulate the box in the right way (akin to an algorithm).

A qubit is physically a subatomic particle, perhaps a photon (a packet of light) or an electron (the basis of electricity). In the case of an electron, angular momentum, or ‘spin,’ is typically used to differentiate between 0 and 1, typically ‘up’ spin and ‘down’ spin. In most Turing computers today, five volts represents 1, and 0 volts represents 0; there is no ambiguity between these states. In quantum computing, we don’t know if the electron has up or down spin until it is measured, thus it has the ability to be in both states at the same time. This isn’t a trick – it is impossible to know its exact state, and thus it represents all states. Crucially, we can affect the characteristics of the particle without having to measure its state through microwave radiation or lasers or magnetic fields. The qubits allow us to store all possibilities in a single register. Quantum ‘gates’ allow us to change the characteristics of these particles, without us having to measure them directly, which means the gates can perform operations on all possibilities at once. Many gates together make a ‘circuit’ or algorithm.

This physical basis of quantum computing is less important to understanding its potential than is its theoretic basis.

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Let’s say we are solving for the factors of a number (what numbers divide leaving no remainder) – for example, 10. Using a Turing machine, one way of finding factors is to continually divide the number to see if it divides with no remainders – does nine fit? No. Does eight fit? No. Does five fit? Yes. So, it is a factor... etc. This is a computational challenge because for each number we are evaluating, we have to cycle through many other numbers to process. Using a quantum computer that has been superposed with all possible states, we can essentially run functions on all possibilities using these gates and determine the output all at once. It’s akin to working with a single row on an Excel spreadsheet compared with a chart of all the outcomes – if we want a quick answer, do we glance at the chart or work through all the rows one by one? In a Turing machine, the function rotates through all the rows; in a quantum machine, the function works on a space that can be visualized as almost a chart of all the possibilities at once.

A quantum algorithm can be thought of as a series of logic gates, with each performing operations on this ‘chart,’ similar to the way an electronic logic gate outputs a single 0 or 1 depending on the state of all the inputs. In reality, each gate is a mathematical function based on imaginary numbers, but the specifics are unimportant for understanding the basic premise.

We can’t see this chart directly. It is an analog of all the choices coded into the superposed state of the subatomic particles. As soon as we try to measure it, just as we measure if the cat is alive or dead, we get a definite output. Of course, at different times, the [???] will be a different value – just like if we measure the state of our cat at the beginning of the experiment, it is more likely to be alive compared with later, when it is more likely to be dead. As such, we must sample the state – we measure it repeatedly and derive a probability distribution. We might find that during our sample, the answer was [101] 80% of the time and [100] 20% of the time. The answer to our problem is thus [101] on average.

Quantum Gates: The Standard Universal Computer Architecture

The common approach to building a quantum computer is gate-based. This approach is commonly called ‘universal,’ as it can be programmed to solve a wide variety of problems and is programmed through the use of quantum gates in a circuit. In traditional computers, operations are performed on binary digits – a NOT gate, for example, turns 0 into 1 and 1 into 0. In gate-based quantum computers, similar operations are performed on the characteristics of the qubit while in a state of superposition. The qubit is yet to have a state when it is processed via the algorithm; rather it is a set of probabilities for what the qubit could be when it is measured. The quantum gates change the characteristics of the particle, without requiring direct measurement of the particle. Put a number of these gates together, and we get a circuit – essentially, an algorithm. Once the gates have operated on the qubit, the qubit is measured so that the range of probabilities collapses to provide an answer. Crucially, these gates must be reversible. In a classic gate, two inputs might lead to one output – for example, in an AND gate, 0 and 0 outputs 1; and 1 and 1 outputs 1. All other combinations output 0. But if we know an output is 1, we can’t determine what the inputs were for certain – we have lost information. This can’t happen in quantum circuits. Upon measurement, the circuit must be able to collapse all the states to

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produce an answer – so reversibility is key. Research continues in both quantum algorithms and the physical hardware that can perform gate functions upon qubits. Typically, these quantum properties of a particle are altered though microwave radiation, which changes the characteristics. A ‘gate’ is the changing of these properties.

Quantum Annealing: A Specific Architecture for Specific Problems

Annealing, as applied to material science, is the process of making a material more workable and stable by heating it and then slowly cooling it in a controlled manner. In glass or metal, this change occurs because molecules in the material align themselves in such a way that imperfections are less likely to occur. Each molecule undergoes thermal fluctuations between hot and cold – the annealing is complete when the material is in a state where the fluctuations deliver the lowest loss in energy. Essentially, the molecules almost ‘collaborate’ to find an optimal solution.

In quantum annealing, quantum particles also seek to align themselves such that a state of minimal energy loss is achieved. The quantum particles experience fluctuations in terms of the energy at a point in space that appears due to uncertainty – when these fluctuations are in a combined state where minimal energy loss is achieved, the fluctuations essentially represent the solution to an equation where the answer to the equation is this minimal energy-loss state. Similar to the material science example, the quantum particles collaborate to find the optimal solution.

Quantum annealing is particularly useful for solving problems that have a discrete number of answers. These problems can typically be expressed as a graph of interconnected binary variables. In some optimization problems, quantum annealing can find the best route through these nodes, such that a desired output state is found.

Compared to gate-based, ‘universal’ quantum computers, quantum annealers require problems to be expressed in a specific format – quadratic unconstrained binary optimization (QUBO) – to leverage the annealing properties of these systems. D-Wave, a company specializing in quantum annealing, claims any classical problem can be written as a QUBO. For some problems, simulating the annealing process in software on classical computers has shown to be as good as implementing the process in hardware.

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2. Success FactorsBenchmark Use Cases

Quantum computing is best at solving problems that take a long time to solve on traditional computers, due to the need to process or consider a huge number of combinations. Imagine an algorithm that, presented with three inputs, takes nine seconds to output the result. With six inputs, the time taken to output a result is 36 seconds. We say this algorithm is in ‘P’ because as the problem gets bigger, the time required to solve it increases Polynomially – the input of inputs, n, takes n2 to solve. With a large number of n, the required time can be huge – too huge to solve in lifetimes, decades or millennia.

Quantum can solve all P problems, usually more speedily than a traditional machine, which is the key area in which quantum computing can add value. But a further area of interest is ‘NP’ problems. NP problems can be verified quickly (so if we guess an answer, we can quickly determine if it’s right), but solving them takes substantial effort over a substantial time. The textbook case is finding the prime factors of a large number (more on this in the Post-Quantum section of this report).

Not all NP problems can be solved more efficiently, however; defining which problems can be efficiently solved with a quantum computer (called bounded-error quantum polynomial time, or BQP, problems) is an ongoing area of research. This viewpoint that a quantum computer would act as a panacea is driving hype (see Figure 3).

Figure 3: Most Overhyped Security BuzzwordsSource: 451 Research’s Voice of the Enterprise: Information Security, Organizational Dynamics 2019Q. Which of the following security trends or buzzwords is the most overhyped? Please select no more than two. Base: All respondents (n=164)

44%39%

22%20%

15%7%

7%6%6%

5%5%

4%1%1%

BlockchainArtificial intelligence (AI)/machine learning (ML)

The Internet of Things (IoT)Quantum computing

Zero trust/beyond corpCloud native

ServerlessContainers

Threat intelligenceUser/entity behavior analytics (UEBA)

MicroservicesAutomation

PlatformOther

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The analysis of the enzyme used by nature to convert hydrogen and nitrogen into ammonia is a gold-medal objective that could reduce global CO2 emissions by up to 3% by doing away with the energy-intense Haber-Bosch process. Nature already can do this conversion at room temperature, but human processes require pressure and energy. The natural enzyme that does this has too many twists and turns for a traditional computer to brute-force unravel – a quantum computer could solve this within hours, as long as it has enough qubits to consider all these permutations. In fact, we think the greatest strength of quantum computing will be in these scientific use cases: unravelling complexities in material science, physics, genomics, chemistry and biochemistry. Why? Because these applications are often associated with distilling a multitude of combinations (be it molecules, forces, sequences or links) into a reduced number of solutions, and they can batch-programmed to execute offline – i.e., they do not need to be operated in real time with operator interaction. Furthermore, the business case is more easily justified in terms of what can be achieved for what level of investigation.

Quantum computers are most likely to be used to solve data-based, investigative problems, rather than for consumer-facing applications, such as blogging or e-commerce. While the prospect of quantum computers outperforming traditional computers is prognosticated by evangelists, quantum computers will exist as subordinate co-processors of traditional computers, similar to how a GPU is used by a CPU for specific tasks at which it excels – not just graphics and video processing, but also physics simulations, geometric computing and scientific computing workloads.

Therefore, quantum computers should be thought of as an entrant in the class of ‘compute accelerators,’ alongside GPUs, FPGAs/ASICs, and in an oblique sense, in-memory computing. Historically, training of machine learning models has been performed on GPUs, with near-term transitions to FPGAs explored in the interest of scalability. In the long term, machine learning models could see greater benefits from quantum computers. A quantum chip could be used to quickly predict an outcome by analyzing millions of permutations in seconds. A traditional computer would perform wrap-around work, from providing an interface to charting results to collecting data, but would use the quantum chip specifically for solving the quantum problem.

Role of Economics

Economics has a massive role to play in the argument for quantum supremacy. What premium would an enterprise pay to solve a problem that would give a meaningful return in hours rather than millennia? Surely a more efficient replacement of the Haber-Bosch process is worth the hefty investment in quantum computing, compared with waiting centuries for traditional simulations to be completed. Today, there is no ‘standard’ price of quantum computing, because each use case is unique, with a significant amount of customization. The US Air Force Research Laboratory signed up to IBM’s Q Network in 2019, committing $7.5m for access to remote quantum computing resources until 2022. This information was released into the public domain as a result of government procurement requirements, but we have no visibility on what this contract includes.

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Google recently claimed to have achieved ‘quantum supremacy’; in its experiment, the company claimed to solve a 50-qubit problem in three minutes using a quantum computer, claiming a traditional computer and algorithm would take 10,000 years. The business case for quantum investment in such a speed-up would be justified here if the output was a useful finding. But IBM claimed that an improved algorithm and better traditional hardware would solve the problem in just 2.5 days. Is a speedup from 2.5 days to three minutes really worth the substantial investment in quantum computing? Of course, it depends on the use case, but it shows that quantum computing only makes sense where traditional computing fails.

Similarly, if quantum computing could be used to solve the configuration of atoms in a medicine for a specific characteristic, or to simulate the flow of gases over a supersonic jet, the acceleration can be economically justifiable if it makes innovations happen in months rather than decades, and brings them to market quicker and provides a business advantage. Quantum might be an academic study that huge corporations are interested in, but money talks. If quantum computing doesn’t give a clear ROI for a specific use case, then so what? Every step taken in quantum’s development is important, and researchers are building on the shoulders of giants, but the product must do something that betters what we have, at a price that is more appropriate than options already in existence today, considering the value added. Concorde could fly passengers from London to New York at twice the speed of jumbo jets of the time, but there are no Concordes in flight today – the premium for the speed simply wasn’t worth the value of any extra few hours at the destination.

To us, useful quantum computing means the solution to the algorithm must provide a financial return that is justified compared with the investment in quantum computing versus the cheaper investment in traditional computing. We are far away from that today (see Figure 4). This justification might become more challenging as FPGA, GPUs, TPUs and other accelerators become more mainstream and can process large datasets quicker and easier than conventional CPUs.

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Figure 4: A Minority of Providers Are Considering Quantum ComputingSource: 451 Research’s Voice of the Service Provider, Infrastructure Evolution 2019Q. Are resources at your company CURRENTLY looking at ways to apply any of these technologies at your company?Base: All respondents (n=225)

Measuring Success

The ‘quantum advantage’ essentially marks the point where quantum computing is commercially viable. In other words, when it provides a meaningful return on the investment. Realistically, we are years away from this development; IBM expects it to take place in the 2020s. But an unexpected challenge is to measure when this point occurs.

‘Quantum supremacy,’ coined in 2012 by CalTech professor John Preskill, loosely means the point at which quantum computing dramatically accelerates finding a solution to a ‘hard’ problem that traditional computing could not have solved in a practical and valuable timeframe – finding a solution in hours compared with lifetimes. This is different from a quantum advantage, which means the problem is solved faster using quantum algorithms but doesn’t make an unsolvable problem solvable.

There is no universal definition on how sped-up the resolution would have to be, nor how hard or useful the problem is. As an analogy, Concorde was a really fast commercial jet, but if we wanted to benchmark it, is it more appropriate to say it’s twice the speed of a 747 or 50x that of the Wright brothers’ first successful flight?

Back in the golden days of the home computers, the likes of Atari, Commodore and Spectrum all battled to be the most enjoyable games platform for the most reasonable price. The key battleground for this war was ‘word size‘ – the length of a single instruction a processor could process. The larger the word a CPU could handle, the more operations could be performed and the greater the size of the pieces of data that could be handled per operation. The first

64%

36%

15%

13%

12%

10%

7%

6%

13%

Computational storage

Memory-driven computing

Quantum computing

Optical computing

Distributed fabrics for heterogeneous computing (Gen-Z, CCIX, etc.)

Alternative storage class memory (e.g., Intel Optane)

Quantum secure communications

Analog neuromorphic computing

Don't know

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commercial home computers were 8-bit, followed by 16-bit, 32-bit and culminating in the mid-1990s with 64-bit. But we haven’t progressed any further than that. Why? Because there is little benefit in adding more bits, when there are so many other aspects of a computer architecture that have a bigger influence on performance. A computer’s performance depends on so many things: word size yes, but also the size and type of memory, the size of the buses used to move data, the architecture of the computer, the cycles per second of the processors (and its cores), and the list goes on and on. There are so many magic numbers that will dictate the performance of a computer that there is no point focusing on just one – all the magic numbers must be optimized and improved.

Qubits are one metric that quantum commentators are focusing on. The more qubits the computer can hold, the larger the problem an operation can work on. For quantum computing to have a quantum advantage or a useful quantum supremacy, more qubits are needed – to crack a 2048-bit RSA key, for example, we’d need more than 4,000 qubits. We can’t break up this big key into smaller problems to make it easier to solve, hence why a large qubit length is needed. But we’d also need other things to make this possible: an architecture that could process the algorithm, stability for a period of time upon which we could measure the output, and resistance to external factors. Like word size in the ’80s, the number of qubits is fundamentally important to the quantum opportunity – but to focus on just that magic number is to be naive to the challenges.

Of course, qubits have an effect on performance – the more data we can store and process in an operation, the more operations can be run in less time. But there are other factors too: how quickly those qubits can be initialized and ready to be encoded, how sensitive the qubits are to one another, how accurate the measurement is of the collapsed state of those qubits, how the circuit is optimized, how many gates run in parallel and so forth. And one of the biggest metrics of quantum performance is error rate. Keeping quantum particles – which by their very nature are small, unpredictable and sensitive to interference – in a state that allows them to be used for computation is difficult, and errors are inevitable. But the smaller the error, the more useful the work that is done using those particles and the more particles (and qubits) can be implemented resiliently.

IBM is proposing Quantum Volume, a holistic measure of a number of metrics that all have a bearing on the overall performance of quantum applications: number of qubits (which is a physical carrier of quantum information, similar to how a bit is a carrier of binary information), the gates used (which are effectively instructions performed on the qubits in the form of an algorithm), connectivity (the interactions between gates), errors on the whole algorithm (not just the single qubits), and the effect of the compiler and software stack. Quantum Volume appears to be a reasonable approach, but it’ll need the support of vendors beyond just IBM to move toward an accepted standard definition – Honeywell too is reporting its performance in Quantum Volume. Classical computing went through a similar process in the 1980s, searching for an objective measure of computing capacity.

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This difficulty in benchmarking quantum computers is exactly why we have steered away from comparisons between vendors and technologies. It is simply too early to compare effectively. As an example, D-Wave claims 2,000 qubits whereas IBM claims 53 qubits – is D-Wave really 40 times better than IBM? Of course not. Each company uses a different architecture, and each architecture may be better at solving different problems. It all depends on the problem and the computer’s ability to solve that problem, not the number of qubits. Just as the various SPEC benchmarks were established in classical computing, quantum practitioners will drive demand for quantum equivalents, eventually.

There are scientists of the opinion that quantum computing is not actually feasible, due to challenges in scaling up while keeping such fragile particles in a manipulatable state. Is quantum computing possibly similar to fusion energy or generic autonomous driving, a siren call no one can resist despite possibly insurmountable difficulties? Our view is that quantum computing is likely to be feasible, just because large, innovation-led companies are making significant investments, while startups continue to be founded and funded. If so, many qualified scientists are willing to make a business justification, then they must have faith in its feasibility.

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Appendix A – Companies Primarily Developing Quantum Computing Hardware and/or SystemsThese companies are developing their own quantum computing hardware platforms, and may already be offering access to these platforms through cloud services or the sale of hardware. Most also offer capabilities beyond hardware, including software development tools and professional services. For simplification, companies producing both hardware and software capabilities are only listed in this table of hardware vendors. Quantum computing is still an area of confidential research, so this table represents a good estimate of the state of the market today, rather than a definitive viewpoint.

NAME HQ INVESTORS VISION PARTNERS

ALIBABA/ALIYUN Hangzhou, China Via its Aliyun subsidiary, Alibaba has launched an 11-qubit quantum computer accessible via the cloud.

Chinese Academy of Sciences

ALPINE QUANTUM TECHNOLOGIES

Innsbruck, Austria €1m from Federation of Industrialists Tirol

€10m from FFG and University of Innsbruck (August 2019)

“Supported by” Austria Wirtschaftsservice Gesellschaft, Start Up Tirol

AQT is working on designing general-purpose quantum information processors that will have a broad range of applications across different industries. The company is focusing on developing these processors for ion-trap devices. It claims to be the first company to realize a controlled-NOT gate operation, a scalable Shor’s algorithm and 14-particle entanglement.

AMAZON Seattle, Washington

See expanded detail in main report

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ATOM COMPUTING Berkeley, California

$5m from National Science Foundation, Venrock, Innovation Endeavors, Prelude Ventures (2018)

Atom Computing aims to build quantum computers though optically controlled quantum bits. These qubits are individual neutral atoms, which Atom Computing claims have long coherence times and dynamic organization capabilities, and are easy to measure. The company believes this is the path toward scalable quantum computing.

AURORA QUANTUM TECHNOLOGIES (QSPICE LABS)

Toronto, Canada $150,000 from Y-Combinator

AuroraQ produces standardized, modular quantum components. These components are optimized by machine learning and are designed to be scalable and integrate seamlessly with quantum processors (i.e., a quantum circuit design tool). The company’s control hardware module will be made from the same materials as the quantum processors and generate signals that send instructions to qubits. AuroraQ claims that its QSpice Design Software is the first circuit design tool in the electronic design automation industry.

Ansys, Creative Destruction Lab, Velocity Garage

BARDEENQ LABS San Francisco, California

BardeenQ Labs is developing non-volatile memories utilizing quantum spin machines, low-power classical devices powered by quantum circuits, and quantum-enabled sensors. BardeenQ Waves, a project by the company, is targeting self-driving cars through ambient temperature quantum devices, vision sensors and quantum AI edge processors.

BLEXIMO Berkeley, California

$1.5m led by Eniac Ventures including Boost VC, Creative Ventures, KEC Ventures, Dmitry Budko, Renata Budko and Gyan Kapur (September 2018)

Bleximo plans to build “quantum accelerators” (quantum-based, application-specific integrated circuits) that work in conjunction with powerful conventional computers to tackle problems that are impractical or even impossible to solve on classical computers alone.

United States Department of Energy, Cyclotron Road, Founder Institute

BRA-KET SCIENCE Austin, Texas Bra-Ket Science claims to be developing a new approach to the problem of practical, room-temperature qubit storage.

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BRANECELL SYSTEMS

Littleton, Massachusetts

$1.8m (March 2015) BraneCell claims it is building a new quantum processing unit that can function at ambient temperatures. The company plans to address the inherent limitations of cryogenic, quantum cloud systems through decentralized quantum computing hardware. It plans to offer quantum services over the cloud and then produce physical quantum computers for sale.

Allegheny Science & Technology (AST)

D-WAVE SYSTEMS Burnaby, Canada See expanded detail in main report

EEROQ New York, New York

EeroQ is utilizing a system in which electrons hover in free space above a surface of liquid helium. It claims that this system is ideal as it does not contain the defects related to other condensed-matter systems. The company says this method is scalable and, due to the long-lived nature of electrons in this environment, good for computations as well.

FUJITSU Tokyo, Japan Fujitsu has a “digital annealer” computer able to solve large-scale combinatorial problems.

GOOGLE Mountain View, California


HITACHI Tokyo, Japan The company focuses on complementary metal-oxide-semiconductor (CMOS) development.

HONEYWELL Charlotte, North Carolina


IBM Armonk, New York See expanded detail in main report

INTEL Santa Clara, California


IONQ College Park, Maryland


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IQM QUANTUM COMPUTERS

Espoo, Finland $12.8m from Matadero QED, Maki.vc, MIG Verwaltungs AG, OpenOcean, Tesi and Vito Ventures (July 2019)

IQM builds quantum processors with a focus on superconducting technology. The company claims that it is establishing new standards in chip architecture for superconducting circuits and developing technology that increases the clock speed of quantum processors.

JoS QUANTUM, VTT Technical Research Centre of Finland, Aalto University

MDR CORPORATION Tokyo, Japan $1.8m (October 2018) MDR Corporation develops quantum hardware for superconducting qubits. This hardware, along with the company’s Python-based software development kit (SDK) named Blueqat, is aimed at enabling simulations on quantum computers that are based on quantum annealing. The company’s objectives revolve around quantum machine learning with use cases in finance, the automotive industry and drug discovery.

D-Wave Systems, ARK Information Systems, DEVEL, Vignette & Clarity, ZYNAS, Rikkyo University

Accelerators: IBM Q Network, NVIDIA, MUFG, KOSE Innovation Program

MICROSOFT See expanded detail in main report

NEXTGENQ Rennes, France NextGenQ is developing quantum computers based on ion-trap technology. The company has identified use cases in artificial intelligence, finance, security and drug design.

NOKIA BELL LABS Murray Hill, New Jersey

The company is researching quantum computing, and has shown an interest in topological computing. The Bell Labs 2019 Prize was awarded to researchers who created a room-temperature system to solve discrete optimization problems using quantum effects. Peter Shor, of Shor’s algorithm fame, was a scientist at Bell Labs.

University of California at Berkley

NTT RESEARCH Palo Alto, California

NTT Research founded a quantum research facility in 2019.

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ORIGIN QUANTUM COMPUTING

Hefei, China Origin Quantum is tackling the full quantum stack by developing quantum hardware and software and delivering these technologies over the cloud. The company has also established the Origin Quantum Industry Alliance, which aims to accelerate the development of quantum computing technology, explore its applications, cultivate the quantum ecosystem and promote the services that quantum computing provides.

Hefei Big Data Assets Operation, Wuhan Digital Engineering Research Institute, Anhui Wentian Quantum Technology, among others

OXFORD QUANTUM CIRCUITS

Oxford, England €2m seed round and an undisclosed Series A from Parkwalk Opportunities Fund and Oxford Sciences Innovation

OQC has developed a fully functional, full stack unit. The company enables quantum computing using its proprietary scalable quantum component, the coaxmon, which utilizes a three-dimensional architecture. This isolates the control wiring from quantum chips and allows for upscaling in qubit arrays without compromising coherence. OQC is currently taking steps to make its product commercially available.

Riverlane, Cambridge Quantum Computing, University of Oxford Leek Lab, Networked Quantum Information Technologies (NQIT)

PSIQUANTUM Berkshire, England $230m venture round from Playground Global

PsiQuantum is building a general-purpose silicon photonic quantum computer. Its computer will encode information in photons instead of electrons, which the company claims provides many benefits on top of the fact that silicon-based technologies are easy to manufacture.

QBLOX Delft, Netherlands Qblox develops technology to enable scalable control of quantum computers including power supplies, a qubit readout module and a qubit control module.

QUANTUM CIRCUITS INC. (QCI)

New Haven, Connecticut


QUANTUM FACTORY Munich, Germany Quantum Factory aims to develop a universally applicable ion-based quantum computer that can operate at room temperature. It then plans to sell its computing power over the cloud. The company is targeting use cases in pharmaceuticals, chemicals and energy.

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QUANTUM MOTION TECHNOLOGIES

Oxford, England Seed round from Parkwalk Opportunities Fund, Oxford Sciences Innovation and IP Group plc

Quantum Motion Technologies is taking advantage of quantum computing architectures that are compatible with standard silicon processing. It believes that this method simplifies the fabrication process and is the path toward truly scalable quantum computers.

QUBITEKK Vista, California $3m from the Department of Energy, Air Force Small Business Innovation Research (SBIR)

Qubitekk focuses on providing hardware components for quantum computing. As of now the company has non-exclusively licensed a single-photon source method for “on-demand” generation of optical qubits. The company also provides quantum research and education as well as quantum cryptography hardware.

QUIX Enschede, The Netherlands

RAPH2Invest, Oost nl, Holding Technopolis Twente

QuiX offers a plug-and-play integrated and reconfigurable light-based (photonic) quantum processor based on silicon-nitride waveguides. This processor is operational at room temperature and can be interfaced with a variety of light sources via fiber connectors. The company also offers a control box that is programmable with software that is provided by QuiX.

QUTECH Delft, The Netherlands

€10m from the Quantum Internet Alliance

QuTech is a research center that aims to develop quantum computers. The company’s research revolves around fault-tolerant quantum computing, quantum internet and networked computing and topological quantum computing. In terms of hardware, QuTech has a strong collaboration with Intel, but the company has also made strides in wet chemical etching to define 1D channels on its own. Along with this research, QuTech utilizes hardware systems with electron spins in quantum dots and superconducting quantum circuits. It has also developed a high-level programming language and compiler (OpenQL), quantum assembly language (QSAM), quantum instruction set architecture (QIAS) and control microarchitecture (QuMA).

Intel, Microsoft, Bluefors, Qblox, Delft University of Technology, Netherlands Organisation for Applied Scientific Research, Dutch Quantum Software Consortium, European Quantum Internet Alliance

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RIGETTI Berkeley, California

$119m from venture capitalists as of July 2019


SILICON QUANTUM COMPUTING (SQC)

Sydney, Australia $83m from the Australian Commonwealth Government, UNSW Sydney, The Commonwealth Bank of Australia, Telstra Corporation and the State Government of New South Wales

Silicon Quantum Computing has three objectives: to demonstrate the capability to reliably produce a 10-qubit prototype quantum integrated processor by 2023, to deliver a programmable device based on a 100-qubit quantum processor embodying error correction before 2030 and to enable access to useful quantum computing solutions for a broad audience by the mid-2030s. The company claims to have built the first silicon two-qubit gate between atom qubits.

SPARROW QUANTUM Copenhagen, Denmark

Sparrow Quantum develops photonic quantum technology components (a diced single-photon chip). The company developed a single-photon source that has a coupling efficiency of more than 98%. The product can be used for quantum simulations, quantum cryptography, quantum optics and quantum networks.

TUNDRASYSTEMS Cardiff, Wales, United Kingdom

TundraSystems plans to develop a complete quantum optical processor, as well as the tools necessary to create it. Use cases for the technology revolve around big data analytics, drug discovery and high-end graphics.

A3Cube Inc., LioniX BV, VLC Photonics, Microsoft BizSpark, Cardiff University, University of Exeter, Imperial College London, University of Southampton, Mellanox Technologies

XANADU Toronto, Canada $45.4m from OMERS Ventures, Georgian Partners, Radical Ventures, Silicon Valley Bank, Tim Draper, Golden Ventures, Real Ventures, BDC and SDTC; grant received from DARPA

Xanadu designs and integrates quantum silicon chips in existing hardware. The company uses photons instead of electrons within its technology. Xanadu also produces the control system that enables the configuration and programming of its chips.

The company also has a cloud offering that incorporates its two main software products, PennyLane (quantum machine learning) and Strawberry Fields (a full-stack Python library for quantum optical circuits).

Creative Destruction Lab, BMO Financial Group and Scotiabank

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Appendix B – Companies/Projects Primarily Focusing on Quantum Computing Software DevelopmentThese companies and projects provide a mechanism for accessing quantum hardware platforms built by third parties, or provide frameworks and/or simulators to aid development. For simplification, these companies are not known to be developing quantum hardware capabilities (those that are doing so are included in Appendix A). Quantum computing is still an area of confidential research, so this table represents a good estimate of the state of the market today, rather than a definitive viewpoint.

General

NAME HQ INVESTORS VISION PARTNERS/ALLIANCES

1QBIT Vancouver, British Columbia


ALIRO TECHNOLOGIES

Harvard University (Cambridge, Massachusetts)

$2.7m from Flybridge Capital Partners, Crosslink Ventures and Samsung NEXT Q Fund (September 2019)

Aliro offers a hardware-independent toolkit for developers of quantum algorithms and applications. The company’s platform is presented as a scalable cloud-based service and offers different hardware-agnostic and noise aware tools to aid the coding process, such as hardware, GUI, REST API, workflow visualization, debugging tools and cross-compilation between high- and low-level languages. This is all done for noisy intermediate-scale quantum (NISQ) devices.

Spinout of NarangLab at Harvard University

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ANYON SYSTEMS Dorval, Quebec, Canada

$33m investment from Canadian government (July 2015)

Anyon Systems designs quantum software and hardware such as control electronics, quantum processors and cryonics systems for early adopters of quantum technology. It plans to introduce software packages and simulation toolkits suitable for the design, simulation and optimization of quantum electronics and quantum devices.

Mitacs

AMAZON WEB SERVICES BRAKET

Seattle, Washington


BLACK BRANE SYSTEMS

Toronto, Canada Black Brane offers a product called Blackstone Virtual Quantum Machine (VQM), a cloud-based, virtualized 128-qubit universal quantum computer that acts as a plug-and-play alternative target machine for the Microsoft Quantum Developer Kit and Q# programs.

CAMBRIDGE QUANTUM COMPUTING LTD

London, United Kingdom

Cambridge Quantum Computing offers a quantum development platform, algorithms for quantum chemistry and machine learning, as well as quantum cybersecurity through an encryption device called IronBridge.

IBM Q Network, Networked Quantum Information Technologies (NQIT) Hub, Pistoia Alliance, UK National Physical Laboratory

CIRQ N/A Cirq is a python framework designed by the Google AI Quantum team for creating noisy intermediate-scale quantum (NISQ) circuits, enabling researchers to write quantum algorithms for specific quantum processors. Cirq is focused on near-term questions and helping researchers understand whether NISQ quantum computers are capable of solving computational problems of practical importance. Cirq is licensed under Apache 2 and is free to be modified or embedded in any commercial or open source package.

Zapata Computing, QC Ware, Quantum Benchmark, Heisenberg Quantum Simulations, Cambridge Quantum Computing, NASA

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HORIZON QUANTUM COMPUTING

Singapore Undisclosed seed funding round led by SGInnovate including investors Abies Ventures, Data Collective, Qubit Protocol, Summer Capital and Posa CV (November 2018)

Horizon Quantum Computing provides design tools to simplify and expedite the process of developing quantum software applications, making quantum application development accessible for software engineers with no prior quantum computing experience, and increasing productivity for quantum computing researchers.

Rigetti (hardware)

HUAWEI Shenzhen, China In 2018, Huawei launched a cloud service platform for quantum computing simulation called HiQ, which includes a quantum computing simulator and a quantum programming framework.

KETITA LABS Tartu, Estonia Ketita Labs is developing hybrid quantum-classical algorithms to enable the use of near-term quantum computers for simulation of quantum chemical systems.

LABBER QUANTUM Cambridge, Massachusetts

Labber Quantum software serves as an interface between the quantum compiler and the physical hardware. It handles instrument control, signal generation, measurement automation, calibration and data storage. The company also provides a graphical user interface as well as a programmatic API for interfacing with instruments and data, automated qubit analysis, calibration and tune-up of qubit parameters. The software is hardware-flexible, designed to work with any qubit modality and instrument vendor.

Keysight Technologies, Chalmers University of Technology, MIT EQuS, IBM Q Network

MICROSOFT AZURE QUANTUM

Redmond, Washington


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M-LABS Hong Kong M-Labs’ core product is Advanced Real-Time Infrastructure for Quantum physics (ARTIQ). It’s an edge control system for quantum information experiments. ARTIQ features a high-level programming language, based on Python, that helps describe complex experiments. M-Labs has also developed a Python-based tool called Migen that automates the VLSI design process.

National Institute of Standards and Technology (NIST), University of Oxford, Joint Quantum Institute, Duke University, The National Metrology Institute of Germany, University of Freiburg, The Leibniz University Hannover, Humboldt University of Berlin, Sun Yat-sen University, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, University of Science and Technology of China, Tsinghua University

PHASE SPACE COMPUTING

Linköping, Sweden Produces educational tools for quantum information science. Offers toolboxes containing electronic circuit boards that approximate the behavior of quantum gates that can be tailored for high school, undergraduate or graduate-level courses.

Spin-off company from Linköping University, Sweden

PROJECTQ N/A ProjectQ is an open source software for quantum computing. Its main features include a high-level language to write quantum programs, a modular and customizable compiler, various hardware and software back ends, and a library (FermiLib) to solve fermionic problems on a quantum computer.

Q-CTRL Sydney, Australia $22m from DCVC, Main Sequence Ventures, Horizons Ventures, Sequoia Capital, Square Peg, Sierra Ventures

Q-CTRL provides cloud-based tools to eliminate decoherence and errors within quantum hardware. Through its software the company give customers the ability to visualize quantum control, utilize python for their tools, access their open source library, and improve algorithm performance.

Bleximo

Q-LION Madrid, Spain The Spanish startup is focused on the development of quantum error-correcting codes to extend the lifetime of logical qubits.

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QDK FOR Q# (MICROSOFT)

Microsoft See expanded detail in main report

QISKIT (IBM) IBM See expanded detail in main report

QUANTASTICA Helsinki, Finland $222,400 from Icebreaker Venture Capital

Quantastica designs software tools to help users transition to hybrid quantum-classical computing. The company offers both programing tools and quantum simulators. Its product, Quantum Programming Studio, is a web-based graphical user interface for designing quantum algorithms and executing them on simulators or quantum computers. Quantastica also designed the Forest back end for Qiskit, which allows users to run Qiskit code on Rigetti simulators and quantum computers.

Rigetti

QUANTUM BENCHMARK

Kitchener, Ontario, Canada

Vanedge Capital Quantum Benchmark provides a software called True-Q Design that enables quantum computer makers to perform error characterization, mitigation and correction as well as performance validation for quantum computing hardware. True-Q Accelerate is designed for quantum computer users as an interface to help synthesize circuits and streamline their experience.

IBM Q Network, Google (supporting development)

QUANTUM MACHINES

Tel Aviv, Israel Battery Ventures, TLV Partners

Quantum Machines develops operation and control systems for quantum computers. The company’s Quantum Orchestration Platform includes several features for users to run complex quantum algorithms and experiments.

QUDOT Fremont, California QuDot simulates universal quantum computers on commodity hardware. QuDot developed the QuDot Virtual Machine, a bytecode VM for quantum computing, to program Qu Dot Nets (QuNets), a Bayesian-based graphical model the company designed to represent qubit states.

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Vertical-Specific

These companies are using quantum computing capabilities to solve specific vertical industry problems rather than creating general-purpose universal computers. They generally use hardware provided by third parties, and provide professional services and vertical-specific tools to solve specific problems. Quantum computing is still an area of confidential research, so this table represents a good estimate of the state of the market today, rather than a definitive viewpoint, and is not an exhaustive list.

NAME HQ INVESTORS TARGET INDUSTRY PARTNERS

APEXQUBIT Amsterdam, The Netherlands

Overkill Ventures, Health Inc

Healthcare, Pharmaceuticals

APPLIEDQUBIT Surrey, United Kingdom

Finance, Industrial, Pharmaceuticals

ARTISTE-QB.NET Toronto, Canada Data Analytics

BEIT.TECH Krakow, Poland $1.4m seed round funding led by Kindred Capital, including Manta Ray Ventures, Firebolt Ventures, Charles M. Songhurst and George Armoyan

New investors: DCVC, Bloomberg Beta and S28

Quantum Computing

BOXCAT Toronto, Canada Bloomberg Beta Graphic Design Creative Destruction Lab, IBM Q Network, S28, Xanadu, D-Wave, Rigetti, DCVC

D SLIT TECHNOLOGIES

Tokyo, Japan Software Development

ELYAH Tokyo, Japan Quantum Software Development

ENTROPICA LABS Singapore Undisclosed Artificial Intelligence, Machine Learning

HQS QUANTUM SIMULATIONS

Karlsruhe, Germany

€2.3m (approx $2.6m) seed round with investors UVC Partners, HTGF and btov Partners

Chemical, Pharmaceuticals “Powered by” CyberForum, Karlsruhe Institute of Technology and Zenkit

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NAME HQ INVESTORS TARGET INDUSTRY PARTNERS

JOS QUANTUM Frankfurt, Germany

Finance, Insurance, Energy NVIDIA Inception Program, IBM Q Network, Heidelberg University, TechQuartier, OpenSuperQ, Quantum Factory, Thinkport, E-shelter, IQM, Quantum Business Network, Hannover Centre for Optical Technologies

MULTIVERSE COMPUTING

San Sebastian, Spain

Creative Destruction Lab, Bic Gipuzkoa Up! Euskadi, Donostiako Sustapena, Basque Department of Education, The Gipuzkoa Provincial Council

Finance Xanadu, Microsoft, Fujitsu

NORDIC QUANTUM COMPUTING GROUP

Oslo, Norway Supporters: Innovation Norway, Google Cloud Platform

Undisclosed seed funding

Finance, Industrial, IT Simonsen Vogt Wiig, University of Oslo

QBITLOGIC Atlanta, Georgia Artificial Intelligence, DevOps, Data Security

Daimler AG, Mercedes-Benz (to utilize CodeAI tech in the production of critical software systems that control its cars)

QU & CO Amsterdam, The Netherlands

Computational Chemistry, Material Discovery, Artificial Intelligence

Schrodinger, IBM, Microsoft, Rigetti, 5-HT Digital Hub

QUANTFI Paris, France Finance

QULAB Los Angeles, California

Cota Capital, Plug and Play, Civilization Ventures, 415, Rising Tide

Healthcare, Pharmaceuticals Rigetti, AWS, Azure

RIVERLANE Cambridge, United Kingdom

€3.25m seed round with investors Cambridge Innovation Capital, Amadeus Capital Partners and Cambridge Enterprise

Quantum Chemistry University of Cambridge, OQC, OpenFermion, University of Bristol, Rigetti

SHYN Sophia, Bulgaria Data Visualization

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